Share and intensity of work current AI systems can materially affect.
SOC 29-2042
Emergency Medical Technicians AI displacement risk
EMTs deliver basic life support in uncontrolled environments — lifting patients, immobilizing spines, driving ambulances, reassuring families. AI dispatch and charting tools organize the work around the care; the physical response is the job.
Likely potential for exposed tasks to move to software after workflow integration.
Compared with paramedics, EMT scope is basic life support, which is even more physical and less protocol-analytical. The occupation's challenges are wages and burnout, not automation; it remains the standard entry rung into EMS.
Distribution
Where Emergency Medical Technicians sits across 620 tracked roles
Displacement pressure 14 — higher than 8% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
12 O*NET task statements matched to SOC 29-2042. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $44,470 (May 2025, US national). The latest BLS row matched SOC 29-2042.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Emergency Medical Technicians
SOC 29-2042 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 14/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Emergency Medical Technicians
The current evidence import matched 12 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks12
SOC29-2042
- n/a task / ID 22852
Administer first aid treatment or life support care to sick or injured persons in prehospital settings.
- n/a task / ID 22853
Assess nature and extent of illness or injury to establish and prioritize medical procedures.
- n/a task / ID 22854
Attend training classes to maintain certification licensure, keep abreast of new developments in the field, or maintain existing knowledge.
- n/a task / ID 22855
Comfort and reassure patients.
- n/a task / ID 22856
Communicate with dispatchers or treatment center personnel to provide information about situation, to arrange reception of survivors, or to receive instructions for further treatment.
- n/a task / ID 22857
Coordinate work with other emergency medical team members or police or fire department personnel.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Administer first aid and life support
Exposure 14, automation 4%, augmentation 24%.
O*NET evidence: Administer first aid treatment or life support care to sick or injured persons in preho... (ID 22852)
Assess and prioritize patient condition
Exposure 26, automation 9%, augmentation 48%.
O*NET evidence: Assess nature and extent of illness or injury to establish and prioritize medical proce... (ID 22853)
Drive and equip emergency vehicles
Exposure 20, automation 8%, augmentation 28%.
O*NET evidence: Drive mobile intensive care unit to specified location, following instructions from eme... (ID 22859)
Report patient information to hospitals
Exposure 46, automation 22%, augmentation 60%.
O*NET evidence: Decontaminate ambulance interior following treatment of patient with infectious disease... (ID 22858)
TaskExposureAutomationAugmentation
Administer first aid and life support144%24%
Assess and prioritize patient condition269%48%
Drive and equip emergency vehicles208%28%
Report patient information to hospitals4622%60%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowParamedic
Training horizon: 12-18 months. Skill overlap 78. Wage preservation signal 140.
- Enter a paramedic program
- Build advanced life support skills
- Log field experience hours
adjacent role
LowEmergency Communications Dispatcher
Training horizon: 3-6 months. Skill overlap 54. Wage preservation signal 122.
- Learn call triage protocols
- Practice multi-agency coordination
- Complete dispatch certification
Comparison guides
Compare the next move before you commit
12-18 months
Emergency Medical Technicians to Paramedic
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Emergency Medical Technicians into Paramedic.
3-6 months
Emergency Medical Technicians to Emergency Communications Dispatcher
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Emergency Medical Technicians into Emergency Communications Dispatcher.
What the AI risk score means for Emergency Medical Technicians
The displacement pressure score for Emergency Medical Technicians is 14. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Report patient information to hospitals carries 22% automation pressure, while Report patient information to hospitals carries 60% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $44,470 (May 2025, US national). Employment context: Entry-level emergency care role with chronic staffing needs. Typical education: Postsecondary certificate plus state licensure.
Wage vulnerability is 66, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Very low displacement pressure
- Staffing shortage persists
- Dispatch AI organizes deployment
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Emergency Medical Technicians, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Basic life support
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Patient assessment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Emergency driving
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Documentation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Paramedic, such as enter a paramedic program.
- By 90 days, compare internal openings and external postings for Paramedic or Emergency Communications Dispatcher and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Emergency Medical Technicians
Will AI replace Emergency Medical Technicians?
EMTs deliver basic life support in uncontrolled environments — lifting patients, immobilizing spines, driving ambulances, reassuring families. AI dispatch and charting tools organize the work around the care; the physical response is the job. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Emergency Medical Technicians work are most exposed to AI?
Report patient information to hospitals and Assess and prioritize patient condition show the strongest automation pressure in this model. Report patient information to hospitals and Assess and prioritize patient condition are better treated as AI-augmented work.
What should Emergency Medical Technicians learn next?
Start with Basic life support, Patient assessment, Emergency driving. The most practical adjacent paths in this model are Paramedic and Emergency Communications Dispatcher.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics